Noninvasive Acute Compartment Syndrome Diagnosis Using Random Forest Machine Learning

Acute compartment syndrome (ACS) is an orthopedic emergency, caused by elevated pressure within a muscle compartment, that leads to permanent tissue damage and eventually death. Diagnosis of ACS relies heavily on patient-reported symptoms, a method that is clinically unreliable and often supplemented with invasive intracompartmental pressure measurements. This study proposes a continuous, objective, noninvasive diagnostic for ACS. The device detects ACS through a random forest machine learning model that uses pressure readings from force-sensitive resistors (FSRs) placed on the skin. The final diagnosis is exported real-time to a web application via Bluetooth. To validate the diagnostic, a data set containing FSR measurements and the corresponding simulated intracompartmental pressure was created. The diagnostic achieved an accuracy, on par to the invasive gold standard, of 97%. The device excelled in key performance metrics including precision, sensitivity, and F1 score. Manufactured for 73 USD, our device may be an economic alternative to needle-based diagnostics. These results demonstrate the potential of noninvasive ACS diagnostics to meet clinical standards and enhance patient care.